Patents by Inventor Michael Wegan

Michael Wegan has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • MACHINE-LEARNING MODELS TO LEVERAGE BEHAVIOR-DEPENDENT PROCESSES
    Publication number: 20230186083
    Abstract: Provided is a process, including: obtaining a first training dataset of subject-entity records; training a first machine-learning model on the first training dataset; forming virtual subject-entity records by appending members of a set of candidate action sequences to time-series of at least some of the subject-entity records; forming a second training dataset by labeling the virtual subject-entity records with predictions of the first machine-learning model; and training a second machine-learning model on the second training dataset.
    Type: Application
    Filed: November 15, 2022
    Publication date: June 15, 2023
    Inventors: Gabriel M. Silberman, Alain Briancon, Gregory Klose, Michael Wegan, Lee Harper, Andrew Kraemer, Arun Prakash
  • Machine-learning models to leverage behavior-dependent processes
    Patent number: 11537878
    Abstract: Provided is a process, including: obtaining a first training dataset of subject-entity records; training a first machine-learning model on the first training dataset; forming virtual subject-entity records by appending members of a set of candidate action sequences to time-series of at least some of the subject-entity records; forming a second training dataset by labeling the virtual subject-entity records with predictions of the first machine-learning model; and training a second machine-learning model on the second training dataset.
    Type: Grant
    Filed: May 9, 2019
    Date of Patent: December 27, 2022
    Assignee: Cerebri AI Inc.
    Inventors: Gabriel M. Silberman, Alain Briançon, Gregory Klose, Michael Wegan, Lee Harper, Andrew Kraemer, Arun Prakash
  • MULTI-STAGE MACHINE-LEARNING MODELS TO CONTROL PATH-DEPENDENT PROCESSES
    Publication number: 20200082261
    Abstract: Provided is a process, including: obtaining a first training dataset of subject-entity records; training a first machine-learning model on the first training dataset; forming virtual subject-entity records by appending members of a set of candidate action sequences to time-series of at least some of the subject-entity records; forming a second training dataset by labeling the virtual subject-entity records with predictions of the first machine-learning model; and training a second machine-learning model on the second training dataset.
    Type: Application
    Filed: May 9, 2019
    Publication date: March 12, 2020
    Inventors: Gabriel M. Silberman, Alain Briançon, Gregory Klose, Michael Wegan, Lee Harper, Andrew Kraemer, Arun Prakash
  • Multi-stage machine-learning models to control path-dependent processes
    Patent number: 10402723
    Abstract: Provided is a process, including: obtaining a first training dataset of subject-entity records; training a first machine-learning model on the first training dataset; forming virtual subject-entity records by appending members of a set of candidate action sequences to time-series of at least some of the subject-entity records; forming a second training dataset by labeling the virtual subject-entity records with predictions of the first machine-learning model; and training a second machine-learning model on the second training dataset.
    Type: Grant
    Filed: September 11, 2018
    Date of Patent: September 3, 2019
    Assignee: Cerebri AI Inc.
    Inventors: Gabriel M. Silberman, Alain Briançon, Gregory Klose, Michael Wegan, Lee Harper, Andrew Kraemer, Arun Prakash